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TEST 123 testing

h1b_2018 = read.csv(file = "./data/dataset.csv")  
## this is a test to read and open the dataset
## I created a folder in our project called "data" 

## then inside it I downloaded the dataset and called it "dataset.csv" 

## then inside it I downloaded the dataset and called it "dataset.csv"


####select the 16 variables which we are interested in
h1b_18_filter = h1b_2018 %>% 
  janitor::clean_names() %>% 
  select(case_number, visa_class, case_status, employer_name, employer_country, employer_city, employer_state,
         naics_code, soc_code, soc_name, total_workers, employment_start_date, employment_end_date, full_time_position,
         prevailing_wage, pw_unit_of_pay, worksite_city, worksite_state, h1b_dependent) %>%
  filter(case_status == "CERTIFIED",
          visa_class == "H-1B",
          employer_country == "UNITED STATES OF AMERICA") %>% 
  select(-visa_class, -case_status, -employer_country)

library(ggplot2)
library(plotly)

h1b_18_filter%>%
  group_by(employer_state) %>%
  summarize(state_n = n())

## # A tibble: 55 x 2
##    employer_state state_n
##    <fct>            <int>
##  1 AK                  74
##  2 AL                1177
##  3 AR                2531
##  4 AZ                3842
##  5 CA               95700
##  6 CO                3208
##  7 CT                5142
##  8 DC                1706
##  9 DE                2370
## 10 FL               16738
## # ... with 45 more rows

h1b_location =
  h1b_18_filter %>% 
  select(case_number, employer_city, employer_state, worksite_city, worksite_state) 

h1b_employer_50 = h1b_location %>% 
  filter(!employer_state %in% c("DC", "GU", "MP", "PR", "VI")) %>% 
  group_by(employer_state) %>% 
  summarize(employer_number = n()) %>% 
  rename("state" = "employer_state")

h1b_worksite_50 = h1b_location %>% 
  filter(!worksite_state %in% c("DC", "GU", "MH",  "MP", "PR", "VI")) %>% 
  group_by(worksite_state) %>% 
  summarize(worksite_number = n()) %>% 
  rename("state" = "worksite_state")


h1b_location_data_big = 
  full_join(h1b_employer_50, h1b_worksite_50, by = "state") %>% 
  filter(worksite_number >= 2000 | employer_number >= 2000) 

## Warning: Column `state` joining factors with different levels, coercing to
## character vector

h1b_location_data_small =
  full_join(h1b_employer_50, h1b_worksite_50, by = "state") %>% 
  filter(worksite_number < 2000 & employer_number < 2000) %>% 
  mutate(employer_number = sum(employer_number),
         worksite_number = sum(worksite_number)) %>% 
  mutate(state = "Others") %>% 
  select(state, employer_number, worksite_number) 

## Warning: Column `state` joining factors with different levels, coercing to
## character vector

others = unique(h1b_location_data_small) 

h1b_location_data = 
  bind_rows(h1b_location_data_big , others)
  

h1b_location_data %>% 
  mutate(state = fct_reorder(state, worksite_number)) %>% 
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  plot_ly(x = ~state, y = ~employer_number, type = 'bar', name = 'headquater location') %>% 
  add_trace(y = ~worksite_number, name = 'work location') %>%
  layout(yaxis = list(title = 'Count'), barmode = 'group', legend = list(x = 0.1, y = 0.9))

======= <<<<<<< HEAD plot_ly(x = ~state, y = ~employer_number, type = 'bar', name = 'headquater location') %>% add_trace(y = ~worksite_number, name = 'work location') %>% layout(yaxis = list(title = 'Count'), barmode = 'group', legend = list(x = 0.1, y = 0.9))

======= plot_ly(x = ~state, y = ~employer_number, type = 'bar', name = 'headquater location') %>% add_trace(y = ~worksite_number, name = 'work location') %>% layout(yaxis = list(title = 'Count'), barmode = 'group', legend = list(x = 0.1, y = 0.9))

>>>>>>> 55da0ffbb28ced05a48a92b1a4967b20008fe176

Map showing number of H1b in worksite state

state_ws = 
  h1b_2018 %>%
  group_by(worksite_state) %>%
  summarize(n = n())
state_ws$hover <- with(state_ws, paste("State", worksite_state, "<br>", "Number", n))
g2 <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
map_2 <- plot_geo(state_ws, locationmode = 'USA-states') %>%
  add_trace(
    z = ~n, text = ~hover, locations = ~worksite_state,
    color = ~n, colors = 'Greens'
  ) %>%
  colorbar(title = "H1b Case Number") %>%
  layout(
    title = '2018 US H1b by worksite State',
    geo = g2
  )
map_2

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Map showing number of H1b in employer state

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Map showing number of H1b in employer state

>>>>>>> 85f70cb7244f7ab6fa2c4ee2931851de7c6549bd >>>>>>> 55da0ffbb28ced05a48a92b1a4967b20008fe176

state_number = 
  h1b_2018 %>%
  group_by(employer_state) %>%
  summarize(n = n())

state_number$hover <- with(state_number, paste("State", employer_state, "<br>", "Number", n))

l <- list(color = toRGB("white"), width = 2)

g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

p <- plot_geo(state_number, locationmode = 'USA-states') %>%
  add_trace(
    z = ~n, text = ~hover, locations = ~employer_state,
    color = ~n, colors = 'Purples'
  ) %>%
  colorbar(title = "Millions USD") %>%
  layout(
    title = '2018 US H1b by State',
    geo = g
  )

p

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h1b_location =
  h1b_18_filter %>% 
  select(case_number, employer_city, employer_state, worksite_city, worksite_state) 

h1b_location_50 = h1b_location %>% 
  filter(employer_state != c("DC", "GU",  "MP", "PR", "VI"))

## Warning in `!=.default`(employer_state, c("DC", "GU", "MP", "PR", "VI")):
## longer object length is not a multiple of shorter object length

## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length

h1b_location_commonwealth =  h1b_location %>% 
  filter(employer_state == c("DC", "GU",  "MP", "PR", "VI"))

## Warning in `==.default`(employer_state, c("DC", "GU", "MP", "PR", "VI")):
## longer object length is not a multiple of shorter object length

## Warning in `==.default`(employer_state, c("DC", "GU", "MP", "PR", "VI")):
## longer object length is not a multiple of shorter object length

h1b_location_50 %>% 
  group_by(employer_state) %>% 
  summarize(n = n()) %>% 
  mutate(employer_state = fct_reorder(employer_state, n)) %>% 
  plot_ly(x = ~employer_state, y = ~n, color = ~employer_state, type = "bar")

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

h1b_location_commonwealth %>% 
  group_by(employer_state) %>% 
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>>>>>>> 55da0ffbb28ced05a48a92b1a4967b20008fe176
##Graph showing top 10 cities with H1b cases in California state (worksite)
city_ca = 
  h1b_2018 %>%
  filter(worksite_state == 'CA') %>% 
  group_by(worksite_city) %>%
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=======
>>>>>>> 85f70cb7244f7ab6fa2c4ee2931851de7c6549bd
>>>>>>> 55da0ffbb28ced05a48a92b1a4967b20008fe176
  summarize(n = n()) %>% 
  filter(min_rank(desc(n)) <= 10)
city_ca %>% 
  mutate(worksite_city = fct_reorder(worksite_city, n)) %>% 
  plot_ly(x = ~worksite_city, y = ~n, color = ~worksite_city, type = "bar")
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
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